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New Technology of Library and Information Service  2015, Vol. 31 Issue (3): 26-32    DOI: 10.11925/infotech.1003-3513.2015.03.04
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Patent Keyword Indexing Based on Weighted Complex Graph Model
Li Junfeng, Lv Xueqiang, Zhou Shaojun
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China
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[Objective] Patent keyword indexing plays an important role in nature language processing and is widely applied in many fields, such as patent retrieval, translation and automatic summary. [Methods] Using K-proximity coupled graph to transfer patents into complex graph model, and average connectivity weight is proposed with the average path variation, the average clustering coefficient, and the current node's liquidity effect. Considering the location information, the word-gap information and the inverse document frequency of keywords, a patent comprehensive correlation calculation method for quantitative analysis of keyword importance is proposed. [Results] Experiment of patent literatures in sensor domain obtains the precision of 60.9% on top-8, and the recall rate of 73.4% on top-10. [Limitations] The result of keywords with low frequency is not good enough, which affects the indexing result. [Conclusions] Experimental results show that this method is effective and has active significance for patent indexing.

Key wordsComplex graph model      Topology potential      Keyword indexing      Average connectivity weight      Comprehensive correlation     
Received: 13 August 2014      Published: 16 April 2015
:  TP391.1  

Cite this article:

Li Junfeng, Lv Xueqiang, Zhou Shaojun. Patent Keyword Indexing Based on Weighted Complex Graph Model. New Technology of Library and Information Service, 2015, 31(3): 26-32.

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